Benefit analysis method and system considering distributed adjustable resource multivariate body

Abstract

The invention discloses a benefit analysis method and system considering distributed adjustable resource multivariate subjects. The method comprises the following steps: analyzing the operation characteristics of a multi-element subject, the multi-element subject comprising a new energy power generator, an energy storage operator, a thermal power generator and an electricity buyer, and establishing a quotation decision model based on a reputation value according to the reputation value and the operation characteristics of the multi-element subject in the electricity transaction; solving the quotation decision-making model through a multi-objective evolutionary algorithm to obtain clearing electric quantity and clearing electricity price, establishing benefit evaluation indexes according to the comprehensive income of the multi-element main body, the output proportion of the new energy main body, the user peak-valley difference and the energy utilization rate, and calculating weight vectors of a plurality of benefit evaluation indexes by using a game theory combination weighting method; and weighing the benefit evaluation indexes through a multi-criterion optimization and compromise solution method to obtain a comprehensive benefit analysis result of the multivariate subjects. According to the scheme, decision scientificity and fairness are improved.

Description

Technical Field

[0001] The present invention belongs to the field of power trading, and in particular relates to a benefit analysis method and system considering multiple subjects of distributed adjustable resources. Background Art

[0002] The traditional single-subject power decision-making model has been unable to cope with new challenges such as the diversification of energy structure and the increase in dispatching demand. The power market without a reputation mechanism may attract some unreliable suppliers or traders who may have dishonest behavior when promising or fulfilling contracts. This situation may lead to irrational resource allocation and reduce the overall efficiency of the market. When the reputation of market participants is not fully considered, unreliable suppliers may cause supply chain disruptions or instability. This instability may affect the reliable supply of electricity and thus the benefits of the overall market. In the power market, the analysis method based on reputation value provides a new perspective for the benefit analysis of multiple subjects such as new energy, energy storage, thermal power generators and users. By comprehensively considering the reputation value of each subject and coordinating their roles and contributions in the market, it is helpful to optimize the overall system benefits and stability, so that the contributions and risks of new energy, energy storage, thermal power generators and users are fairly evaluated. By considering the reputation history of each subject, the transparency of resource allocation and market competition is ensured, and the unfairness caused by information asymmetry is reduced. The benefit analysis method can dynamically identify the subjects with good benefits in the market and allocate resources more efficiently to new energy and energy storage projects with high reputation and strong management capabilities. It encourages all entities to continuously improve their operation and management levels, and promotes the improvement of the overall market benefits. The reputation value can help the dispatch center better understand the response capabilities and performance reliability of each entity, so as to more flexibly select participating entities during dispatch and peak load regulation. Reputable new energy, energy storage and thermal power generators can provide more stable output and services during peak periods, which helps to ease the dispatch pressure of the power grid and improve the flexibility and safety of the system. Policymakers and market regulators can design and implement incentive mechanisms more scientifically, guiding market entities to pay attention to long-term social responsibility and environmental protection while pursuing economic benefits. This diversified benefit analysis promotes the sustainable development of the power market and ensures the healthy growth of emerging technologies such as new energy and energy storage in the market.

[0003] In summary, the multi-subject benefit analysis method based on reputation value provides an effective way to coordinate and optimize the benefits among new energy, energy storage, thermal power generators and users in the power market. This method not only improves market efficiency and transparency, but also enhances the overall stability and sustainable development capabilities of the system. Summary of the invention

[0004] In order to solve the deficiencies in the prior art, the present invention provides a benefit analysis method and system considering multiple subjects of distributed adjustable resources, analyzing benefits from multiple angles, improving the scientificity and fairness of decision-making, and realizing dynamic adjustment.

[0005] In order to solve the above technical problems, the present invention adopts the following technical solutions.

[0006] The present invention first discloses a benefit analysis method considering multiple entities of distributed adjustable resources, the method comprising the following steps:

[0007] Analyze the operating characteristics of multiple entities, including new energy power generators, energy storage operators, thermal power generators and power buyers, and establish a quotation decision model based on reputation values ​​according to the reputation values ​​of the multiple entities in power trading and the operating characteristics of the multiple entities;

[0008] The quotation decision model based on reputation value is solved by a multi-objective evolutionary algorithm to obtain the cleared electricity volume and cleared electricity price, and the benefit evaluation index is established according to the comprehensive benefits of multiple entities, the output proportion of new energy entities, the peak-to-valley difference of users and the energy utilization rate, and the weight vector of multiple benefit evaluation indicators is calculated using the game theory combined weighting method;

[0009] The benefit evaluation indicators are weighed through multi-criteria optimization and compromise solution methods to obtain the comprehensive benefit analysis results of multiple subjects.

[0010] The present invention further includes the following preferred embodiments:

[0011] The step of establishing a quotation decision model based on the reputation value of the multiple subjects in the power transaction and the operating characteristics of the multiple subjects further includes establishing a quotation decision model for a new energy power producer through the following process:

[0012] Calculate wind power output power P wt :

[0013]

[0014] Where P we is the rated power of wind power generation; v in 、v out 、v e They correspond to the starting wind speed, shutdown wind speed and rated wind speed of the wind turbine respectively; v t is the wind speed of the wind turbine at time t;

[0015] Calculate the photovoltaic output power P pvt :

[0016] P pvt =SF t ηpv

[0017] Where S is the area of ​​the photovoltaic panel; F t is the light intensity at time t; η pv is the conversion efficiency of solar panels;

[0018] Set the light energy intensity F it and wind speed v within N days it The standard, i = 1, 2, ..., N, obtains the wind power and solar photovoltaic power output characteristic change curve (P wi1 ,P wi2 ,…,P wi24 )、(P pvi1 ,P pvi2 ,...,P pvi24 ) Combine the output curves of wind power and photovoltaic power into ω i =[(P wi1 ,P pvi1 ),(P wi2 ,P pvi2 ),…,(P wi24,pvi24 )];

[0019] Apply the K-means method to determine the number of clusters K:

[0020]

[0021] Where B(ω) is the average clustering effect; B(ω i ) is the clustering effect of the i-th class; c(ω i ) is the separation degree of the cluster, which represents the average distance between the daily effort curve and the curves in all scenes; d(ω i ) is the cohesion, which is the average distance between the daily effort curve and the curves in the same scene;

[0022] From N wind power and photovoltaic output curve samples, select any one ω 01 As the initial mean curve, take the remaining samples with ω 01 The furthest apart curve ω 02 As the new initial cluster center, the initial cluster center is J0 = (ω 01 ,ω 02 ); then calculate the distance between other curves and the cluster center curve, and obtain the new cluster center J0=(ω 01 ,ω 02 ,…,ω 0K );

[0023] Iterate the scene division and form new cluster centers, and stop when the clustering result remains unchanged; let scene u k The hourly order of electricity generation sequence X = (x1, x2, ..., xs ), then its output range is [x min ,x max ]; scene u k The specific output for a certain hour is:

[0024]

[0025] where y i For scene u k The specific output value of

[0026] When processing the output sequence, if the number of output elements is odd, the permutation X = (x1, x2, ..., x s ) is used as the output; when the number of output elements is an even number, the average of the two middle numbers is calculated and taken as the specific output value;

[0027] The objective function of new energy power generators is expressed as:

[0028]

[0029] Where T is the total trading period; is the market clearing electricity price at time t under scenario w; is the winning bid power and actual output at time t in scenario w; c n is the price coefficient of maintenance cost for new energy generators; p n The subsidy price for new energy is lower than the winning bid power;

[0030] Calculate the comprehensive quotation of new energy power generators:

[0031]

[0032] in is the initial quotation of the new energy power generator; κ is the weight coefficient; C min , C max , are the minimum and maximum values ​​of the credibility value and the credibility value of the generator respectively; λ′ is the market reference price.

[0033] The step of establishing a quotation decision model based on the reputation value of the multiple subjects in the power transaction and the operating characteristics of the multiple subjects further includes establishing a quotation decision model for thermal power generators through the following process:

[0034] Calculate the net income of thermal power generators at time g:

[0035]

[0036] in They are the economic benefits, operating costs, start-up costs and environmental costs of thermal power generators at time g; is the power generation of the thermal power generator at time t; is the long-term electricity sales price of thermal power generation; a TP , b TP 、c TP is the coal consumption coefficient of thermal power generators; is the coal price; m TP B is the operating loss coefficient of the thermal power generating unit; TP Purchase cost of power generating units; is the number of rotor cycles of the thermal power generator at time t; u TP_ON Start-up costs for thermal power generators; The emission cost of the ath type of pollutant per unit output of the thermal power generating unit; is the pollution emission coefficient of thermal power generators;

[0037] Introduce a credit value evaluation mechanism to calculate the comprehensive quotation of thermal power generators:

[0038]

[0039] in for; is the initial bid of the thermal power generator; α is the weight coefficient; C min , C max , are the minimum and maximum values ​​of the credibility value and the credibility value 0 of the generator; λ′ is the market reference price.

[0040] The step of establishing a quotation decision model based on the reputation value of the multiple subjects in the power transaction and the operating characteristics of the multiple subjects further includes establishing a quotation decision model for energy storage operators through the following process:

[0041] Calculating the Net Benefits of Energy Storage Systems

[0042]

[0043]

[0044] in They are the peak shaving benefits of the energy storage system, electricity consumption during low-price periods, and operating, depreciation and loss costs; is the state variable, It indicates the peak period, and vice versa, it indicates the trough period; They are the peak-shaving benefit and valley-filling cost of the energy storage system at time t respectively; They are the peak load and valley load at time t respectively; They are the unit operation cost and maintenance cost of the energy storage system respectively; is the rated power of the energy storage system; is the capacity of the energy storage system; They are the unit loss cost and depreciation expense of the energy storage system respectively;

[0045] Integrate reputation value into the quotation mechanism to build a comprehensive quotation model:

[0046]

[0047] in Comprehensive quotes for energy storage operators; is the initial bid of the energy storage operator; β is the weight coefficient; C min , are the minimum credit value and the credit value of the generator respectively.

[0048] The step of establishing a quotation decision model based on reputation values ​​of the multiple entities in power trading and the operating characteristics of the multiple entities further includes establishing a quotation decision model for electricity buyers through the following process:

[0049] The user's energy cost Calculated as the cost of purchasing electricity Demand response benefits The difference:

[0050]

[0051] in are the electricity purchase amount and unit electricity purchase cost of the user at time t respectively;

[0052] Construct a dissatisfaction function model to quantitatively describe the cost of user response to demand:

[0053]

[0054]

[0055] in is the user's demand response at time t; The unit demand response compensation cost for the user at time t; is the user's demand response willingness coefficient; The unit opportunity cost of providing demand response to customers; are the coefficients of the user's reduction-type demand response and absorption-type demand response respectively; is the state variable, Indicates that the user performs a reduction-type demand response at time t, otherwise, performs an absorption-type demand response;

[0056] Introducing dynamic adjustment coefficient Quantitatively measure the actual influence of instantaneous demand response behavior on long-term demand response behavior:

[0057]

[0058] in To consider the user demand response under the coefficient change;

[0059] Get the corrected user's objective function for:

[0060]

[0061] Calculate the comprehensive quote of the online shopping website

[0062]

[0063] in is the initial quotation of the e-commerce shopping platform; λ is the market reference price; τ is the weight coefficient; The reputation value of the e-commerce shopping website.

[0064] The benefit evaluation index is established based on the comprehensive benefits of multiple entities, the output ratio of new energy entities, the peak-to-valley difference of users and the energy utilization rate, further including:

[0065] Calculate the comprehensive income E of multiple entities t Total revenue for renewable energy power generation, thermal power, users and energy storage operators:

[0066]

[0067] in are the revenues of renewable energy power generation, thermal power generation and energy storage operators at time t; Compensate the user's demand response cost at time t;

[0068] Calculate the output ratio H of new energy generators t :

[0069]

[0070] in are the output values ​​of wind power and photovoltaic power respectively;

[0071] Calculate the user's peak-to-valley difference A t :

[0072]

[0073] in are the maximum and minimum values ​​of user load respectively;

[0074] Calculate energy utilization rate X t :

[0075]

[0076] in are the energy usage and total energy supply in period t respectively.

[0077] The weighing of the benefit evaluation indexes by multi-criteria optimization and compromise solution method further includes:

[0078] The VIKOR evaluation method is used for comprehensive analysis. By comprehensively evaluating the ideal solution, the group benefits are maximized and the individual satisfaction is optimized. The strategy closest to the ideal solution is selected as the compromise decision-making solution, which is as follows:

[0079] Calculate the positive and negative ideal solutions for each indicator

[0080]

[0081] where x ij is the score of the j evaluation object under the i-th evaluation index, 0≤x ij ≤1;

[0082] Calculate the maximum group benefit S j and minimize the individual regret value R j :

[0083]

[0084] Calculate the comprehensive benefit value Q of each evaluation object j :

[0085]

[0086] Where S + , S - Respectively represent S j , the maximum and minimum values; R + , R - Respectively represent R j The maximum and minimum values ​​of ;λ is the decision mechanism coefficient;

[0087] If λ<0.5, it means that the decision is made based on minimizing the individual regret value; if λ>0.5, it means that the decision is made based on maximizing the group utility; if λ=0.5, it means that the decision is made comprehensively according to the equilibrium principle.

[0088] The evaluation object is based on Sj , R j and Q j Sort the values;

[0089] When Q j When the evaluation object with the smallest value meets the following two conditions, the Q j The evaluation object with the smallest value is the optimal value;

[0090] Condition 1:

[0091] Where A1 and A2 are the optimal and suboptimal evaluation objects, and f is the number of evaluation objects;

[0092] Condition 2: A1 follows S j and R j When sorting in ascending order, it also meets the first ranking;

[0093] When condition 1 is not met, a compromise solution is adopted, that is, A1, A2, …, A k One of them, A k For the last satisfaction evaluation object; when the evaluation object does not meet condition 2, select A1 or A2.

[0094] The present invention also discloses a system for analyzing the benefits of distributed adjustable resources by using the aforementioned method for analyzing the benefits of distributed adjustable resources by considering multiple subjects, including:

[0095] A model building module is used to analyze the operating characteristics of multiple entities, including new energy power generators, energy storage operators, thermal power generators and power buyers, and establish a quotation decision model based on reputation values ​​according to the reputation values ​​of the multiple entities in power trading and the operating characteristics of the multiple entities;

[0096] An evaluation index establishment module is used to solve the quotation decision model based on reputation value through a multi-objective evolutionary algorithm to obtain the cleared electricity volume and cleared electricity price, establish benefit evaluation indicators according to the comprehensive benefits of multiple entities, the output proportion of new energy entities, the peak-to-valley difference of users and the energy utilization rate, and use the game theory combined weighting method to calculate the weight vectors of multiple benefit evaluation indicators;

[0097] The comprehensive analysis module is used to weigh the benefit evaluation indicators through multi-criteria optimization and compromise solution methods to obtain comprehensive benefit analysis results of multiple subjects.

[0098] Accordingly, the present application also discloses a terminal, including a processor and a storage medium;

[0099] The storage medium is used to store instructions;

[0100] The processor is used to operate according to the instructions to execute the steps of the aforementioned benefit analysis method considering multiple entities of distributed adjustable resources.

[0101] Correspondingly, the present application also discloses a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps of the aforementioned benefit analysis method considering multiple subjects of distributed adjustable resources are implemented.

[0102] The beneficial effect of the present invention is that, compared with the prior art, the present invention provides a benefit analysis method and system that considers the multiple subjects of distributed adjustable resources, comprehensively evaluates the benefits of various adjustable resources through multi-angle and multi-dimensional analysis, and improves the scientificity and fairness of decision-making; integrates different types of resources such as distributed power generation, energy storage systems and demand response, and systematically analyzes their benefit performance in different scenarios, providing policy makers and operators with a comprehensive and accurate basis for decision-making; uses game theory to combine weighting methods to reduce the deviation caused by a single evaluation standard and ensure that the decision-making process is transparent and fair; emphasizes dynamic adjustment capabilities, real-time updates and adjustments, adapts to changes in data and conditions, and promptly responds to market changes and policy adjustments to maintain efficient operation of the system. Through multi-angle analysis, the scientificity and fairness of decision-making are improved, dynamic adjustment and optimal configuration are achieved, and support is provided for the sustainable development and efficient utilization of the new energy field. BRIEF DESCRIPTION OF THE DRAWINGS

[0103] Figure 1 It is a logic framework diagram of benefit analysis considering multiple subjects of distributed adjustable resources in the present invention;

[0104] Figure 2 It is a schematic diagram of the benefit analysis calculation process considering multiple entities of distributed adjustable resources in the present invention. DETAILED DESCRIPTION

[0105] In order to make the purpose, technical solution and advantages of the present invention more clear, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0106] The embodiments described in this application are only some embodiments of the present invention, not all embodiments. Based on the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without creative work are all within the protection scope of the present invention.

[0107] In view of the shortcomings of the prior art, the present invention proposes a benefit analysis method and system considering multiple entities of distributed adjustable resources, see Figure 1 As shown, the following steps are included:

[0108] Step 1: Analyze the operating characteristics of multiple entities, including new energy power generators, energy storage operators, thermal power generators and power buyers, and establish a quotation decision model based on reputation values ​​according to the reputation values ​​of the multiple entities in power trading and the operating characteristics of the multiple entities.

[0109] New energy power generators, thermal power generators, energy storage operators, and users all have their own decision variables, and the initial bidding strategy model is constructed based on their decision variables. Due to their own characteristics, the weight proportion of reputation value is not the same. Therefore, the comprehensive bidding model of thermal power generators, energy storage operators, and users is as follows.

[0110] First, the uncertainty of wind power output prediction is mainly caused by wind speed, so wind power generation is greatly affected by wind speed and rated value. wt for:

[0111]

[0112] Where P we is the rated power of wind power generation; v in 、v out 、v e They correspond to the starting wind speed, shutdown wind speed and rated wind speed of the wind turbine respectively; v t is the wind speed of the wind turbine at time t.

[0113] Photovoltaic output power is related to the area of ​​the panel and the light-to-conversion efficiency. The output power P pvt for:

[0114] P pvt =SF t η pv

[0115] Where S is the area of ​​the photovoltaic panel; F t is the light intensity at time t; η pv The conversion efficiency of solar panels.

[0116] Set the light energy intensity F it and wind speed v within N days it The standard, i = 1, 2, ..., N, obtains the wind power and solar photovoltaic power output characteristic change curve (P wi1 ,P wi2 ,…,P wi24 )、(P pvi1 ,P pvi2 ,...,P pvi24 ) Combine the output curves of wind power and photovoltaic power into ω i =[(P wi1 ,P pvi1 ),(P wi2,P pvi2 ),…,(P wi24,pvi24 )].

[0117] The K-means method is applied to the scenarios of reducing wind power, photovoltaic and other renewable energy fields, and then the interval theory is used to generate the new energy output interval.

[0118] First, determine the number of clusters K:

[0119]

[0120] Where B(ω) is the average clustering effect; B(ω i ) is the clustering effect of the i-th class; c(ω i ) is the separation degree of the cluster, which represents the average distance between the daily effort curve and the curves in all scenes; d(ω i ) is the cohesion, which is the average distance between the daily effort curve and the curves in the same scene.

[0121] Secondly, determine the initial cluster center and select a random line ω from N wind power and photovoltaic output curve samples 01 As the initial mean curve, take the remaining samples with ω 01 The furthest apart curve ω 02 As the new initial cluster center, the initial cluster center is J0 = (ω 01 ,ω 02 ); then calculate the distance between other curves and the cluster center curve, and obtain the new cluster center J0=(ω 01 ,ω 02 ,…,ω 0K ).

[0122] Finally, the scene is divided and new cluster centers are formed iteratively, and the process stops when the clustering result remains unchanged. k The hourly order of electricity generation sequence X = (x1, x2, ..., x s ), then its output range is [x min ,x max ]. Scene u k The specific output for a certain hour is:

[0123]

[0124] where y i For scene u k The specific output value.

[0125] When processing the output sequence, if the number of output elements is odd, the permutation X = (x1, x2, ..., x s) is used as the output; when the number of output elements is an even number, the average of the two middle numbers is calculated and taken as the specific output value.

[0126] The bids of new energy power generators in the electricity market face uncertainty, which is due to the intermittent and unstable nature of new energy power generation (such as wind power and photovoltaic power generation). This uncertainty brings significant risks to the bidding strategies of power generators: if the actual power generation is lower than the bid output, the power generator will face penalties from the independent system operator (ISO), resulting in additional penalty costs; and if the bid output exceeds the actual available power, it will cause waste of wind and power, resulting in the loss of opportunity costs.

[0127] In order to balance these risk factors in the quotation, new energy power generators need to consider various economic factors, including electricity revenue, operation and maintenance costs, electricity purchase expenses, sales revenue and possible penalties. Therefore, the goal of power generators is to maximize their own interests through reasonable bidding strategies. The objective function of new energy power generators is expressed as:

[0128]

[0129] Wherein T is the total trading period, and in the preferred embodiment, T=24; is the market clearing electricity price at time t under scenario w; is the winning bid power and actual output at time t in scenario w; c n is the price coefficient of maintenance cost for new energy generators; p n The subsidy price for new energy is lower than the winning bid power.

[0130] The introduction of a credit value mechanism in renewable energy power generators has a significant impact on their quotations. Power generators with high credit values ​​are able to obtain more favorable quotations and lower transaction costs in the electricity market due to their reliable performance records and stable power generation capacity. This is because high credit values ​​reduce the market's concerns about their default risks and enhance market trust, thereby improving their market competitiveness and bargaining power. On the contrary, power generators with low credit values ​​may face higher transaction costs and lower quotations because the market has a higher expectation of their default risks and needs to compensate for potential risk losses by increasing their quotations:

[0131]

[0132] in Provide comprehensive quotations for new energy power generators; is the initial quotation of the new energy power generator; κ is the weight coefficient, and in the preferred embodiment, κ=0.7; C min , are the minimum value, minimum value and credibility value of the generator respectively; λ′ is the market reference price.

[0133] Thermal power generation companies are committed to maximizing net profit by optimizing the objective function, where net profit is defined as the difference between sales revenue and total costs. Sales revenue mainly comes from electricity sales, while cost accounting covers operating costs, initial startup costs, and environmental impact costs that cannot be ignored. Specifically:

[0134]

[0135] in is the net income of the thermal power generator at time g; They are the economic benefits, operating costs, start-up costs and environmental costs of thermal power generators at time g; is the power generation of the thermal power generator at time t; is the electricity selling price of thermal power generators; a TP , b TP 、c TP is the coal consumption coefficient of thermal power generators; is the coal price; m TP B is the operating loss coefficient of the thermal power generating unit; TP Purchase cost of power generating units; is the number of rotor cycles of the thermal power generator at time t;

[0136] is the state variable of the thermal power unit at time g, To start the thermal power unit, The thermal power unit has not started; TP_ON Start-up costs for thermal power generators; The emission cost of the ath type of pollutant per unit output of the thermal power generating unit; is the pollution emission coefficient of thermal power generators.

[0137] The net income of thermal power generators in the electricity market is After the introduction of the reputation value evaluation mechanism, the quotation will be adjusted according to its reputation value to reflect the comprehensive reputation of the thermal power generator in terms of historical performance, service quality, market performance, etc. The comprehensive quotation not only considers economic factors but also incorporates the reputation history of the generator by associating the initial quotation with the reputation value. In this way, the comprehensive quotation of the thermal power generator can more accurately represent its overall competitiveness and reliability in the market, thereby obtaining more reasonable and fair pricing in market transactions:

[0138]

[0139] in Comprehensive quotations for thermal power generators; is the initial bid of the thermal power generator; α is the weight coefficient, preferably, α=0.6; C min , C max , are the minimum and maximum values ​​of the credibility value and the credibility value of the generator respectively; λ′ is the market reference price.

[0140] The operation of thermal power generators is subject to power upper and lower limits, start and stop duration, and spinning reserve constraints:

[0141]

[0142] in They are the minimum load and the maximum output of thermal power generators under variable load;

[0143] They are respectively the operation duration g hours and the shutdown duration g hours of the thermal power generator at time t-1; They are used to determine the minimum start-up and shutdown time g of thermal power generators respectively; They are positive and negative spinning reserve capacity respectively.

[0144] Energy storage operators use energy storage technology to absorb excess energy during the low electricity demand period and store it to fill the valley, and then release the stored energy during the peak period to balance the supply, thereby reducing the peak. This strategy benefits from the price difference between peak and valley electricity prices, allowing energy storage operators to effectively utilize this price difference to achieve economic benefits. Therefore, in the current context, energy storage operators use energy storage capacity as a core decision-making factor, aiming to optimize the net benefits of energy storage systems. Maximize your goals:

[0145]

[0146]

[0147] in They are the peak shaving benefits of the energy storage system, electricity consumption during low-price periods, and operating, depreciation and loss costs; is the state variable, It means it is in the peak period, and vice versa is the trough period; They are the peak-shaving benefit and valley-filling cost of the energy storage system at time t respectively; They are the peak load and valley load at time t respectively; They are the unit operation cost and maintenance cost of the energy storage system respectively; is the rated power of the energy storage system; is the capacity of the energy storage system; They are the unit loss cost and depreciation expense of the energy storage system respectively.

[0148] By analyzing the objective function of the energy storage company's bidding behavior, the initial bidding strategy of the energy storage company is obtained. This initial bidding objective function is mainly based on factors such as the energy storage company's cost, market demand, and technical parameters, providing a basis for its pricing in the market. However, in order to more accurately reflect the market behavior and reputation of the energy storage company, the key factor of reputation value is introduced.

[0149] The introduction of reputation value is to comprehensively consider the performance of energy storage providers in past transactions, including their historical records of performance, service quality, response speed, etc. By integrating reputation value into the quotation mechanism, a new comprehensive quotation model can be constructed, so that the quotation can not only reflect the basic economic cost, but also reflect the market reputation and long-term performance of energy storage providers. The new comprehensive quotation is as follows:

[0150]

[0151] in Comprehensive quotes for energy storage operators; is the initial quotation of the energy storage operator; β is the weight coefficient, preferably β = 0.3; C min , C max , are the minimum value, minimum value and credibility value of the generator respectively; λ′ is the market reference price.

[0152] Energy storage operators are subject to the constraints of energy storage and prescribed energy release scope:

[0153]

[0154] in They are the minimum and maximum limits of energy release of the energy storage system respectively; are the minimum and maximum energy storage of the energy storage system respectively.

[0155] The user takes the demand response amount as the decision variable and the energy cost Minimum is the objective function. The user's energy cost is the electricity purchase cost Demand response benefits The difference:

[0156]

[0157] in are the user’s purchased electricity quantity and unit electricity purchase cost at time t respectively.

[0158] Demand response benefits for users Revenue from demand response compensation Demand response costs The cost-effectiveness of demand response is inseparable from its impact on user satisfaction, and user satisfaction is determined by both a strong willingness to respond and efficient actual response capabilities. Willingness to respond refers specifically to the amount of service voluntarily provided by users within a specific period of time, which reflects the user's positive attitude and degree of cooperation; while response capability refers to the scale of demand response that users can actually provide within the same period of time. This value is affected by the user's existing energy usage habits, energy dependence, and possible adjustment space. By constructing a dissatisfaction function model, the cost of user demand response is quantitatively described. Specifically:

[0159]

[0160] in is the user's demand response at time t; The unit demand response compensation cost for the user at time t; is the user's demand response willingness coefficient; The unit opportunity cost of providing demand response to customers; are the coefficients of the user's reduction-type demand response and absorption-type demand response respectively; is the state variable, It indicates that the user performs a reduction-type demand response at time t, otherwise, an absorption-type demand response is performed.

[0161] However, according to the law of diminishing marginal utility, when users frequently implement demand response to the same scenario, their satisfaction with subsequent responses may gradually decrease. However, the current traditional demand response mechanism often ignores the dynamic characteristics of time and fails to fully consider how the current demand response behavior may affect the demand response effect in subsequent periods. Therefore, in order to adapt to the rapidly changing environment, a dynamic adjustment coefficient is introduced. Used to improve the traditional demand response model. The actual influence of the demand response behavior at instantaneous time i on the demand response behavior in the long term time t is quantitatively measured. After incorporating the influence of the dynamic change coefficient, the user's response volume presents the characteristics of dynamic adjustment as follows:

[0162]

[0163] in It is the user demand response considering the coefficient change.

[0164] The corrected user objective function can be obtained for:

[0165]

[0166] In summary, through the analysis of the objective function, we can derive the initial bidding strategy of users in the electricity market. This initial bidding reflects the user's expectation of the transaction price without considering other external factors.

[0167] However, in order to more comprehensively evaluate the user's competitiveness and actual performance in the market, the important parameter of reputation value is introduced. The reputation value reflects comprehensive factors such as the user's performance record in historical transactions, service quality and market performance. Specifically, the comprehensive quotation is a combination of the initial quotation and the user's reputation value, so that the quotation is not only based on market demand and cost considerations, but also fully reflects the user's reputation status and long-term performance in the market. The comprehensive quotation strategy is as follows:

[0168]

[0169] in Provide comprehensive quotes for e-commerce buyers; is the initial quotation of the e-shopping merchant; λ is the market reference price; τ is the weight coefficient, and in the preferred embodiment, τ=0.7; The reputation value of the e-commerce shopping website.

[0170] Users are also subject to upper and lower limits such as the demand response willingness coefficient and the demand response amount:

[0171]

[0172] in are the minimum and maximum values ​​of the user's demand response at time t, respectively.

[0173] Step 2: Solve the quotation decision model based on reputation value through multi-objective evolutionary algorithm to obtain clearing electricity quantity and clearing electricity price, establish benefit evaluation index according to the comprehensive benefits of multiple subjects, the output proportion of new energy subjects, the peak-to-valley difference of users and energy utilization rate, and use the game theory combined weighting method to calculate the weight vector of multiple benefit evaluation indicators.

[0174] In view of the interactive influence between the decision-making objectives of various energy entities, this paper aims to seek the optimal overall comprehensive benefit maximization strategy. Therefore, for the decision-making problem of multiple energy entities, its optimization solution process naturally evolves into a complex task of multi-objective optimization. In the solution strategy of multi-objective optimization problem, a multi-objective evolutionary algorithm is used to solve it.

[0175] By examining the comprehensive benefit indicators of multiple entities, the environmental protection perspective relies on the proportion of new energy in the total output, safety is evaluated by analyzing the difference in peak and valley electricity consumption among users, and efficiency is quantitatively reflected by energy efficiency data. This multi-dimensional evaluation method aims to provide a comprehensive perspective to explore its potential impact.

[0176] In the electricity market, comprehensive analysis and utilization of the comprehensive benefits of multiple entities including new energy, conventional units, energy storage companies and users can improve energy utilization and system reliability, reduce operating costs, promote the development of renewable energy, achieve win-win results for all parties, promote electricity market reform, and ultimately achieve multiple goals of economic, environmental and social benefits. t Total revenue for renewable energy power generation, thermal power, users and energy storage operators:

[0177]

[0178] in are the revenues of renewable energy power generation, thermal power generation and energy storage operators at time t; Compensate the user's demand response cost at time t.

[0179] Analyzing the output ratio of new energy entities can optimize power dispatch, improve the capacity of new energy consumption, enhance market competitiveness, evaluate environmental benefits, increase user participation, and promote the continuous progress of new energy. t is the proportion of wind energy and photovoltaic power, and the expression is:

[0180]

[0181] in They are the output values ​​of wind power and photovoltaic power respectively.

[0182] Analyzing user peak-to-valley differences helps optimize power dispatch and cost management, improve grid stability, promote demand-side management and energy storage development, formulate precise marketing strategies, and improve energy utilization, thereby achieving efficient and sustainable operation of the power system.

[0183] The peak-to-valley difference of the user t is the difference between the maximum and minimum values ​​of user load:

[0184]

[0185] in are the maximum and minimum values ​​of user load respectively.

[0186] Energy utilization index helps optimize resource allocation, improve system efficiency, reduce operating costs, support decision-making, enhance environmental benefits, and enhance user participation, thereby achieving efficient and sustainable development of the energy system. t is the ratio of energy output to input:

[0187]

[0188] in are the energy usage and total energy supply in period t respectively.

[0189] The combined weighting method based on game theory analyzes comprehensive benefits, which helps to improve the fairness and rationality of decision-making, optimize resource allocation and promote cooperation, enhance the scientific nature of decision-making, enhance dynamic adjustment capabilities, and provide multi-dimensional analysis and support, thereby optimizing the overall benefits and ultimately promoting the efficient and sustainable development of the system.

[0190] Determine the weight set combination. Assume that there are N weight calculation methods, and obtain the weight results w of the indicators respectively k =[w k1 ,w k2 ,…,w km ], where k = 1, 2,…, m is the number of evaluation indicators.

[0191] The difference between weights is balanced through the combined weighting method to achieve the best result and minimize the deviation between weights. Based on the basic idea of ​​combined weighting in game theory, the deviation between weights is minimized. The calculation formula is:

[0192]

[0193] Solve for the optimal combination coefficient a k . Using the matrix differential characteristics, we can solve the first-order derivative condition of the optimal solution and calculate the correlation coefficients {a1, a2, …, a N}

[0194]

[0195] Right now:

[0196]

[0197] Calculate the combined weight w * , the specific calculation formula is:

[0198]

[0199] Step 3: Weigh the benefit evaluation indicators through multi-criteria optimization and compromise solution methods to obtain the comprehensive benefit analysis results of multiple subjects.

[0200] In order to comprehensively and accurately evaluate the comprehensive impact of renewable energy after large-scale access, such as its contribution to the efficiency of thermal power generators, the stability of the power system, the user-end power experience and the efficiency of energy storage equipment, the VIKOR evaluation method is used for comprehensive analysis. A multi-index optimization method is adopted, with the ideal point solution as the theoretical basis. By comprehensively evaluating the advantages and disadvantages of the ideal solution, the group benefits are maximized and the individual satisfaction is optimized, and the strategy closest to the ideal solution is selected as the compromise decision plan, which is as follows:

[0201] Calculate the positive and negative ideal solutions for each indicator

[0202]

[0203] where x ij is the score of the j evaluation object under the i-th evaluation index, 0≤x ij ≤1.

[0204] Calculate the maximum group benefit S j and minimize the individual regret value R j :

[0205]

[0206] Calculate the comprehensive benefit value Q of each evaluation object j :

[0207]

[0208] Where S + , S - Respectively represent S j , the maximum and minimum values; w i is the combined weight; R + , R - Respectively represent R j The maximum and minimum values ​​of ; λ is the decision mechanism coefficient.

[0209] If λ<0.5, it means that the decision is made based on minimizing the individual regret value; if λ>0.5, it means that the decision is made based on maximizing the group utility; if λ=0.5, it means that the decision is made comprehensively according to the equilibrium principle. In the preferred embodiment, λ=0.5.

[0210] The evaluation object is based on S j , R j and Q j Sort the values.

[0211] When Q j When the evaluation object with the smallest value meets the following two conditions, the Q jThe evaluation object with the smallest value is the optimal value.

[0212] Condition 1:

[0213] A1 and A2 are the optimal and suboptimal evaluation objects, and f is the number of evaluation objects.

[0214] Condition 2: A1 follows S j and R j The first position is also satisfied when sorting in ascending order.

[0215] When condition 1 is not met, a compromise solution can be adopted, that is, A1, A2, …, A k One of them, A k For the last satisfaction evaluation object; when the evaluation object does not meet condition 2, select A1 or A2.

[0216] The beneficial effect of the present invention is that, compared with the prior art, the present invention provides a benefit analysis method and system that considers the multiple subjects of distributed adjustable resources, comprehensively evaluates the benefits of various adjustable resources through multi-angle and multi-dimensional analysis, and improves the scientificity and fairness of decision-making; integrates different types of resources such as distributed power generation, energy storage systems and demand response, and systematically analyzes their benefit performance in different scenarios, providing policy makers and operators with a comprehensive and accurate basis for decision-making; uses game theory to combine weighting methods to reduce the deviation caused by a single evaluation standard and ensure that the decision-making process is transparent and fair; emphasizes dynamic adjustment capabilities, real-time updates and adjustments, adapts to changes in data and conditions, and promptly responds to market changes and policy adjustments to maintain efficient operation of the system. Through multi-angle analysis, the scientificity and fairness of decision-making are improved, dynamic adjustment and optimal configuration are achieved, and support is provided for the sustainable development and efficient utilization of the new energy field.

[0217] The present invention may be a system, a method and / or a computer program product. The present invention also discloses a system for analyzing the benefits of distributed adjustable resources based on the aforementioned method for analyzing the benefits of distributed adjustable resources based on multiple subjects, comprising:

[0218] A model building module is used to analyze the operating characteristics of multiple entities, including new energy power generators, energy storage operators, thermal power generators and power buyers, and establish a quotation decision model based on reputation values ​​according to the reputation values ​​of the multiple entities in power trading and the operating characteristics of the multiple entities;

[0219] An evaluation index establishment module is used to solve the quotation decision model based on reputation value through a multi-objective evolutionary algorithm to obtain the cleared electricity volume and cleared electricity price, establish benefit evaluation indicators according to the comprehensive benefits of multiple entities, the output proportion of new energy entities, the peak-to-valley difference of users and the energy utilization rate, and use the game theory combined weighting method to calculate the weight vectors of multiple benefit evaluation indicators;

[0220] The comprehensive analysis module is used to weigh the benefit evaluation indicators through multi-criteria optimization and compromise solution methods to obtain comprehensive benefit analysis results of multiple subjects.

[0221] Based on the spirit of the present invention, those skilled in the art can easily think that a computer program product can be obtained based on the aforementioned benefit analysis method considering multiple subjects of distributed adjustable resources. The computer program product may include a computer-readable storage medium, which carries computer-readable program instructions for enabling a processor to implement various aspects of the present disclosure. That is, the present application also includes a terminal, including a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the aforementioned benefit analysis method considering multiple subjects of distributed adjustable resources.

[0222] Computer readable storage medium can be a tangible device that can keep and store the instructions used by the instruction execution device. Computer readable storage medium can be, for example, - but not limited to - electrical storage device, magnetic storage device, optical storage device, electromagnetic storage device, semiconductor storage device or any suitable combination of the above. More specific examples (non-exhaustive list) of computer readable storage medium include: portable computer disk, hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disk read-only memory (CD-ROM), digital versatile disk (DVD), memory stick, floppy disk, mechanical encoding device, for example, punch card or groove protrusion structure with instructions stored thereon and any suitable combination of the above. Computer readable storage medium used here is not interpreted as instantaneous signal itself, such as radio wave or other free propagating electromagnetic wave, electromagnetic wave propagated by waveguide or other transmission medium (for example, light pulse by optical fiber cable) or electrical signal transmitted by wire.

[0223] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0224] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages-such as Smalltalk, C++, etc., and conventional procedural programming languages-such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network-including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA) may be personalized by utilizing the state information of a computer-readable program instruction, and the electronic circuit may execute a computer-readable program instruction, thereby realizing various aspects of the present disclosure.

[0225] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A benefit analysis method considering multiple entities of distributed adjustable resources, characterized in that: include: Analyze the operating characteristics of multiple entities, where the multiple entities include new energy power generators, energy storage operators, thermal power generators and power buyers, and establish a quotation decision model based on reputation values ​​according to the reputation values ​​of the multiple entities in power trading and the operating characteristics of the multiple entities; The quotation decision model based on reputation value is solved by a multi-objective evolutionary algorithm to obtain the cleared electricity volume and cleared electricity price, and the benefit evaluation index is established according to the comprehensive benefits of multiple entities, the output proportion of new energy entities, the peak-to-valley difference of users and the energy utilization rate, and the weight vector of multiple benefit evaluation indicators is calculated using the game theory combined weighting method; The benefit evaluation indicators are weighed through multi-criteria optimization and compromise solution methods to obtain the comprehensive benefit analysis results of multiple subjects.

2. The benefit analysis method considering multiple entities of distributed adjustable resources according to claim 1 is characterized in that: The step of establishing a quotation decision model based on the reputation value of the multiple subjects in the power transaction and the operating characteristics of the multiple subjects further includes establishing a quotation decision model for a new energy power producer through the following process: Calculate wind power output power P wt : Where P we is the rated power of wind power generation; v in 、v out 、v e They correspond to the starting wind speed, shutdown wind speed and rated wind speed of the wind turbine respectively; v t is the wind speed of the wind turbine at time t; Calculate the photovoltaic output power P pvt : P pvt =SF t η pv Where S is the area of ​​the photovoltaic panel; F t is the light intensity at time t; η pv The conversion efficiency of solar panels; Set the light energy intensity F it and wind speed v within N days it The standard, i = 1, 2, ..., N, obtains the wind power and solar photovoltaic power output characteristic change curve (P wi1 ,P wi2 ,…,P wi24 )、(P pvi1 ,P pvi2 ,...,P pvi24 ) Combine the output curves of wind power and photovoltaic power into ω i =[(P wi1 ,P pvi1 ),(P wi2 ,P pvi2 ),…,(P wi24 ,pvi24)]; Apply the K-means method to determine the number of clusters K: Where B(ω) is the average clustering effect; B(ω i ) is the clustering effect of the i-th class; c(ω i ) is the separation degree of the cluster, which represents the average distance between the daily effort curve and the curves in all scenes; d(ω i ) is the cohesion, which is the average distance between the daily effort curve and the curves in the same scene; Choose any one ω from N splines 01 As the initial mean curve, take the remaining samples with ω 01 The furthest apart curve ω 02 As the new initial cluster center, the initial cluster center is J0 = (ω 01 ,ω 02 ); then calculate the distance between other curves and the cluster center curve, and obtain the new cluster center J0=(ω 01 ,ω 02 ,…,ω 0K ); Iterate the scene division and form new cluster centers, and stop when the clustering result remains unchanged; let scene u k The hourly order of electricity generation sequence X = (x1, x2, ..., x s ), then its output range is [x min ,x max ]; scene u k The specific output for a certain hour is: where y i For scene u k The specific output value of When processing the output sequence, if the number of output elements is odd, the permutation X = (x1, x2, ..., x s ) is used as the output; when the number of output elements is an even number, the average of the two middle numbers is calculated and taken as the specific output value; The objective function of new energy generators is expressed as: Where T is the total trading period; is the market clearing electricity price at time t under scenario w; is the winning bid power and actual output at time t in scenario w; c n is the price coefficient of maintenance cost for new energy generators; p n The subsidy price for new energy is lower than the winning bid power; Calculate the comprehensive quotation of new energy power generators: in is the initial quotation of the new energy power generator; κ is the weight coefficient; C min , are the minimum credit value and the credit value of the generator respectively.

3. The benefit analysis method considering multiple entities of distributed adjustable resources according to claim 2 is characterized in that: The step of establishing a quotation decision model based on the reputation value of the multiple subjects in the power transaction and the operating characteristics of the multiple subjects further includes establishing a quotation decision model for thermal power generators through the following process: Calculate the net income of thermal power generators at time g: in They are the economic benefits, operating costs, start-up costs and environmental costs of thermal power generators at time g; is the power generation of the thermal power generator at time t; is the long-term electricity sales price of thermal power generation; a TP , b TP 、c TP is the coal consumption coefficient of thermal power generators; is the coal price; m TP B is the operating loss coefficient of the thermal power generating unit; TP Purchase cost of power generating units; is the number of rotor cycles of the thermal power generator at time t; u TP_ON Start-up costs for thermal power generators; The emission cost of the ath type of pollutant per unit output of the thermal power generating unit; is the pollution emission coefficient of thermal power generators; Introduce a credit value evaluation mechanism to calculate the comprehensive quotation of thermal power generators: in for; is the initial bid of the thermal power generator; α is the weight coefficient; C min , C max , They are the minimum and maximum credit values ​​and the credit value of the generator respectively.

4. The benefit analysis method considering multiple entities of distributed adjustable resources according to claim 3 is characterized in that: The step of establishing a quotation decision model based on the reputation value of the multiple subjects in the power transaction and the operating characteristics of the multiple subjects further includes establishing a quotation decision model for energy storage operators through the following process: Calculating the Net Benefits of Energy Storage Systems in They are the peak shaving benefits of the energy storage system, electricity consumption during low-price periods, and operating, depreciation and loss costs; is the state variable, It indicates the peak period, and vice versa, it indicates the trough period; They are the peak-shaving benefit and valley-filling cost of the energy storage system at time t respectively; They are the peak load and valley load at time t respectively; They are the unit operation cost and maintenance cost of the energy storage system respectively; is the rated power of the energy storage system; is the capacity of the energy storage system; They are the unit loss cost and depreciation expense of the energy storage system respectively; Integrate reputation value into the quotation mechanism to build a comprehensive quotation model: in Comprehensive quotes for energy storage operators; is the initial bid of the energy storage operator; β is the weight coefficient; C min , C max , They are the minimum and maximum credit values ​​and the credit value of the generator respectively.

5. The benefit analysis method considering multiple entities of distributed adjustable resources according to claim 4 is characterized in that: The step of establishing a quotation decision model based on reputation values ​​of the multiple entities in power trading and the operating characteristics of the multiple entities further includes establishing a quotation decision model for electricity buyers through the following process: The user's energy cost Calculated as the cost of purchasing electricity Demand response benefits The difference: in are the electricity purchase amount and unit electricity purchase cost of the user at time t respectively; Construct a dissatisfaction function model to quantitatively describe the cost of user response to demand: in is the user's demand response at time t; The unit demand response compensation cost for the user at time t; is the user's demand response willingness coefficient; The unit opportunity cost of providing demand response to customers; are the coefficients of the user's reduction-type demand response and absorption-type demand response respectively; is the state variable, Indicates that the user performs a reduction-type demand response at time t, otherwise, performs an absorption-type demand response; Introducing dynamic adjustment coefficient Quantitatively measure the actual influence of instantaneous demand response behavior on long-term demand response behavior: in To consider the user demand response under the coefficient change; Get the corrected user's objective function for: Calculate the comprehensive quote of the online shopping website in Provide the initial quotation for the e-commerce buyer; λ is the market reference price; τ is the weight coefficient; The reputation value of the e-commerce shopping website.

6. The benefit analysis method considering multiple entities of distributed adjustable resources according to claim 5 is characterized in that: The benefit evaluation index is established based on the comprehensive benefits of multiple entities, the output ratio of new energy entities, the peak-to-valley difference of users and the energy utilization rate, further including: Calculate the comprehensive income E of multiple entities t Total revenue for renewable energy power generation, thermal power, users and energy storage operators: in are the revenues of renewable energy power generation, thermal power generation and energy storage operators at time t; Compensate the user's demand response cost at time t; Calculate the output ratio H of new energy generators t : in are the output values ​​of wind power and photovoltaic power respectively; Calculate the user's peak-to-valley difference A t : in are the maximum and minimum values ​​of user load respectively; Calculate energy utilization rate X t : in are the energy usage and total energy supply in period t respectively.

7. The benefit analysis method considering multiple entities of distributed adjustable resources according to claim 6 is characterized in that: The weighing of the benefit evaluation indexes by multi-criteria optimization and compromise solution method further includes: The VIKOR evaluation method is used for comprehensive analysis. By comprehensively evaluating the ideal solution, the group benefits are maximized and the individual satisfaction is optimized. The strategy closest to the ideal solution is selected as the compromise decision-making solution, which is as follows: Calculate the positive and negative ideal solutions for each indicator where x ij is the score of the j evaluation object under the i-th evaluation index, 0≤x ij ≤1; Calculate the maximum group benefit S j and minimize the individual regret value R j : Calculate the comprehensive benefit value Q of each evaluation object j : Where S + , S - Respectively represent S j , the maximum and minimum values; R + , R - Respectively represent R j The maximum and minimum values ​​of ;λ is the decision mechanism coefficient; If λ<0.5, it means that the decision is made based on minimizing the individual regret value; if λ>0.5, it means that the decision is made based on maximizing the group utility; if λ=0.5, it means that the decision is made comprehensively according to the equilibrium principle. The evaluation object is based on S j , R j and Q j Sort the values; When Q j When the evaluation object with the smallest value meets the following two conditions, the Q j The evaluation object with the smallest value is the optimal value; Condition 1: Where A1 and A2 are the optimal and suboptimal evaluation objects, and f is the number of evaluation objects; Condition 2: A1 follows S j and R j It also satisfies the ranking first when sorting; When condition 1 is not met, a compromise solution is adopted, that is, A1, A2, …, A k One of them, A k For the last satisfaction evaluation object; when the evaluation object does not meet condition 2, select A1 or A2.

8. A benefit analysis system considering multiple entities of distributed adjustable resources, characterized in that: include: A model building module is used to analyze the operating characteristics of multiple entities, including new energy power generators, energy storage operators, thermal power generators and power buyers, and establish a quotation decision model based on reputation values ​​according to the reputation values ​​of the multiple entities in power trading and the operating characteristics of the multiple entities; An evaluation index establishment module is used to solve the quotation decision model based on reputation value through a multi-objective evolutionary algorithm to obtain the cleared electricity volume and cleared electricity price, establish benefit evaluation indicators according to the comprehensive benefits of multiple entities, the output proportion of new energy entities, the peak-to-valley difference of users and the energy utilization rate, and use the game theory combined weighting method to calculate the weight vectors of multiple benefit evaluation indicators; The comprehensive analysis module is used to weigh the benefit evaluation indicators through multi-criteria optimization and compromise solution methods to obtain comprehensive benefit analysis results of multiple subjects.

9. The benefit analysis system considering multiple entities of distributed adjustable resources according to claim 8 is characterized in that: The model building module is further used to establish a quotation decision model for new energy power generators through the following process: Calculate wind power output power P wt : Where P we is the rated power of wind power generation; v in 、v out 、v e They correspond to the starting wind speed, shutdown wind speed and rated wind speed of the wind turbine respectively; vt is the wind speed of the wind turbine generator set at time t; Calculate the photovoltaic output power P pvt : P pvt =SF t η pv Where S is the area of ​​the photovoltaic panel; F t is the light intensity at time t; η pv The conversion efficiency of solar panels; Set the light energy intensity F it and wind speed v within N days it The standard, i = 1, 2, ..., N, obtains the wind power and solar photovoltaic power output characteristic change curve (P wi1 ,P wi2 ,…,P wi24 )、(P pvi1 ,P pvi2 ,...,P pvi24 ) Combine the output curves of wind power and photovoltaic power into ω i =[(P wi1 ,P pvi1 ),(P wi2 ,P pvi2 ),……,(P wi24 ,pvi24)]; Apply the K-means method to determine the number of clusters K: Where B(ω) is the average clustering effect; B(ω i ) is the clustering effect of the i-th class; c(ω i ) is the separation degree of the cluster, which represents the average distance between the daily effort curve and the curves in all scenes; d(ω i ) is the cohesion, which is the average distance between the daily effort curve and the curves in the same scene; Choose any one ω from N splines 01 As the initial mean curve, take the remaining samples with ω 01 The furthest apart curve ω 02 As the new initial cluster center, the initial cluster center is J0 = (ω 01 ,ω 02 ); then calculate the distance between other curves and the cluster center curve, and obtain the new cluster center J0=(ω 01 ,ω 02 ,…,ω 0K ); Iterate the scene division and form new cluster centers, and stop when the clustering result remains unchanged; let scene u k The hourly order of electricity generation sequence X = (x1, x2, ..., x s ), then its output range is [x min ,x max ]; scene u k The specific output for a certain hour is: where y i For scene u k The specific output value of When processing the output sequence, if the number of output elements is odd, the permutation X = (x1, x2, ..., x s ) is used as the output; when the number of output elements is an even number, the average of the two middle numbers is calculated and taken as the specific output value; The objective function of new energy generators is expressed as: Where T is the total trading period; is the market clearing electricity price at time t under scenario w; is the winning bid power and actual output at time t in scenario w; c n is the price coefficient of maintenance cost for new energy generators; p n The subsidy price for new energy is lower than the winning bid power; Calculate the comprehensive quotation of new energy power generators: in is the initial quotation of the new energy power generator; κ is the weight coefficient; C min , are the minimum credit value and the credit value of the generator respectively.

10. The benefit analysis system considering multiple entities of distributed adjustable resources according to claim 9 is characterized in that: The model building module is further used to establish a bidding decision model for thermal power generators through the following process: Calculate the net income of thermal power generators at time g: in They are the economic benefits, operating costs, start-up costs and environmental costs of thermal power generators at time g; is the power generation of the thermal power generator at time t; is the long-term electricity sales price of thermal power generation; a TP , b TP 、c TP is the coal consumption coefficient of thermal power generators; is the coal price; m TP B is the operating loss coefficient of the thermal power generating unit; TP Purchase cost of power generating units; is the number of rotor cycles of the thermal power generator at time t; u TP_ON Start-up costs for thermal power generators; The emission cost of the ath type of pollutant per unit output of the thermal power generating unit; is the pollution emission coefficient of thermal power generators; Introduce a credit value evaluation mechanism to calculate the comprehensive quotation of thermal power generators: in for; is the initial bid of the thermal power generator; α is the weight coefficient; C min , C max , They are the minimum and maximum credit values ​​and the credit value of the generator respectively.

11. The benefit analysis system considering multiple subjects of distributed adjustable resources according to claim 10 is characterized in that: The model building module is further used to establish a bidding decision model for energy storage operators through the following process: Calculating the Net Benefits of Energy Storage Systems in They are the peak shaving benefits of the energy storage system, electricity consumption during low-price periods, and operating, depreciation and loss costs; is the state variable, It indicates the peak period, and vice versa, it indicates the trough period; They are the peak-shaving benefit and valley-filling cost of the energy storage system at time t respectively; They are the peak load and valley load at time t respectively; They are the unit operation cost and maintenance cost of the energy storage system respectively; is the rated power of the energy storage system; is the capacity of the energy storage system; They are the unit loss cost and depreciation expense of the energy storage system respectively; Integrate reputation value into the quotation mechanism to build a comprehensive quotation model: in Comprehensive quotes for energy storage operators; is the initial bid of the energy storage operator; β is the weight coefficient; C min , C max , They are the minimum and maximum credit values ​​and the credit value of the generator respectively.

12. The benefit analysis system considering multiple entities of distributed adjustable resources according to claim 11, characterized in that: The model building module is further used to establish a quotation decision model for the e-commerce shopping platform through the following process: The user's energy cost Calculated as the cost of purchasing electricity Demand response benefits The difference: in are the electricity purchase amount and unit electricity purchase cost of the user at time t respectively; Construct a dissatisfaction function model to quantitatively describe the cost of user response to demand: in is the user's demand response at time t; The unit demand response compensation cost for the user at time t; is the user's demand response willingness coefficient; The unit opportunity cost of providing demand response to customers; are the coefficients of the user's reduction-type demand response and absorption-type demand response respectively; is the state variable, Indicates that the user performs a reduction-type demand response at time t, otherwise, performs an absorption-type demand response; Introducing dynamic adjustment coefficient Quantitatively measure the actual influence of instantaneous demand response behavior on long-term demand response behavior: in To consider the user demand response under the coefficient change; Get the corrected user's objective function for: Calculate the comprehensive quote of the online shopping website in Provide the initial quotation for the e-commerce buyer; λ is the market reference price; τ is the weight coefficient; The reputation value of the e-commerce shopping website.

13. The benefit analysis system considering multiple entities of distributed adjustable resources according to claim 12 is characterized in that: The evaluation index establishment module is further used to: Calculate the comprehensive income E of multiple entities t Total revenue for renewable energy power generation, thermal power, users and energy storage operators: in are the revenues of renewable energy power generation, thermal power generation and energy storage operators at time t; Compensate the user's demand response cost at time t; Calculate the output ratio H of new energy generators t : in are the output values ​​of wind power and photovoltaic power respectively; Calculate the user's peak-to-valley difference A t : in are the maximum and minimum values ​​of user load respectively; Calculate energy utilization rate X t : in are the energy usage and total energy supply in period t respectively.

14. The benefit analysis system considering multiple entities of distributed adjustable resources according to claim 13, characterized in that: The comprehensive analysis module is further used for: The VIKOR evaluation method is used for comprehensive analysis. By comprehensively evaluating the ideal solution, the group benefits are maximized and the individual satisfaction is optimized. The strategy closest to the ideal solution is selected as the compromise decision-making solution, which is as follows: Calculate the positive and negative ideal solutions for each indicator where x ij is the score of the j evaluation object under the i-th evaluation index, 0≤x ij ≤1; Calculate the maximum group benefit S j and minimize the individual regret value R j : Calculate the comprehensive benefit value Q of each evaluation object j : Where S + , S - Respectively represent S j , the maximum and minimum values; R + , R - Respectively represent R j The maximum and minimum values ​​of ; λ is the decision mechanism coefficient; If λ<0.5, it means that the decision is made based on minimizing the individual regret value; if λ>0.5, it means that the decision is made based on maximizing the group utility; if λ=0.5, it means that the decision is made comprehensively according to the equilibrium principle. The evaluation object is based on S j , R j and Q j Sort the values; When Q j When the evaluation object with the smallest value meets the following two conditions, the Q j The evaluation object with the smallest value is the optimal value; Condition 1: Where A1 and A2 are the optimal and suboptimal evaluation objects, and f is the number of evaluation objects; Condition 2: A1 follows S j and R j It also satisfies the ranking first when sorting; When condition 1 is not met, a compromise solution is adopted, that is, A1, A2, ..., A k One of them, A k For the last satisfaction evaluation object; when the evaluation object does not meet condition 2, select A1 or A2.

15. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is used to operate according to the instructions to execute the steps of the benefit analysis method considering distributed adjustable resource multiple subjects according to any one of claims 1-7.

16. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the benefit analysis method considering multiple subjects of distributed adjustable resources as described in any one of claims 1-7 are implemented.

Images